Dynamic image correction and wavefront sensing with a digital holographic sensor using 4D implicit neural representations

In this work, we develop a dynamic framework for image sharpening and wavefront sensing from a video sequence of data collected by a digital holographic sensor. We build on recent progress using Implicit Neural Representations (INRs) to model complex phase aberrations caused by atmospheric turbulence. INRs leverage unsupervised learning to form a continuous functional representation of the phase aberrations due to turbulence. In this work, we extend our framework from modeling 3D wavefronts from single DH measurements (consisting of three spatial coordinates, <i>x, y, z</i>), to modeling 4D wavefronts from a time sequence of DH measurements (consisting of three spatial coordinates plus time, <i>x, y, z, t</i>). By introducing additional time-varying measurements, we can learn dynamic behavior of the atmospheric turbulence. Additionally, this temporal information helps to better constrain our under determined estimation problem, improving estimation quality. We demonstrate these benefits using wave-optics simulations.